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Zentralanstalt für Meteorologie und Geodynamik Calibrating the ECMWF EPS 2m Temperature and 10m Wind Speed for Austrian Stations Sabine Radanovics.

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Presentation on theme: "Zentralanstalt für Meteorologie und Geodynamik Calibrating the ECMWF EPS 2m Temperature and 10m Wind Speed for Austrian Stations Sabine Radanovics."— Presentation transcript:

1 Zentralanstalt für Meteorologie und Geodynamik Calibrating the ECMWF EPS 2m Temperature and 10m Wind Speed for Austrian Stations Sabine Radanovics

2 Zentralanstalt für Meteorologie und Geodynamik Outline Motivation Methods Verification Results Summary (Präsentation) Folie 2

3 Zentralanstalt für Meteorologie und Geodynamik Motivation: ECMWF EPS T2m Innsbruck OBS Folie 3

4 Zentralanstalt für Meteorologie und Geodynamik Motivation: Talagrand Diagram T2m +24h (Präsentation) Folie

5 Zentralanstalt für Meteorologie und Geodynamik Methods: 2m Temperature Non-homogenous Gaussian Regression (NGR) (Gneiting et. al 2005) Folie 5

6 Zentralanstalt für Meteorologie und Geodynamik Methods: 10m Wind Speed Cut – Off NGR (Thorarinsdottir und Gneiting 2008) Logistic Regression No assumption of a distribution Probability, that a threshold is exeeded Least square minimisation (Präsentation) Folie 6

7 Zentralanstalt für Meteorologie und Geodynamik Verification Results: Talagrand Diagram T2m +24h (Präsentation) Folie 7

8 Zentralanstalt für Meteorologie und Geodynamik Verification Results: T2m Bias, RMSE, Spread (Präsentation) Folie

9 Zentralanstalt für Meteorologie und Geodynamik Verification Results: CRPS T2m Folie –

10 Zentralanstalt für Meteorologie und Geodynamik Verification Results: 10m Wind Speed CRPS (Präsentation) Folie –

11 Zentralanstalt für Meteorologie und Geodynamik Verification Results: 10m Wind Speed CRPS (Präsentation) Folie –

12 Zentralanstalt für Meteorologie und Geodynamik Verification Results: ff10m – Brier Score 1m/sBrier Score 5m/s Folie 12

13 Zentralanstalt für Meteorologie und Geodynamik Summary Improvement of verification scores Tested different amounts of training data Tested two different methods for wind speed Tested predictors for logistic regression Ensemble mean wind speed is a good predictor Ensemble spread of the wind speed does not improve results Wind direction does not improve results Better probability forecasts with calibrated EPS Folie 13

14 Zentralanstalt für Meteorologie und Geodynamik Literature Tilmann Gneiting and others. Calibrated probabilistic forecasting using ensemble model output statistics and minimum crps estimation. Monthly Weather Review, 133:1098– 1118, Thordis L. Thorarinsdottir and Tilmann Gneiting. Probabilistic forecasts of wind speed: Ensemble model output statistics using heteroskedastic censored regression. Technical Report 546, Department of Statistics, University of Washington, Folie 14


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